利用卫星影像自动校准无人机视觉惯性系统，长期保持高精度定位。
SCAR: Satellite Imagery-Based Calibration for Aerial Recordings
- 用公开卫星图与高程模型匹配空中图像，自动生成标定参数。
- 两年多六次大规模飞行测试，重投影误差显著降低，姿态精度大幅提升。
- 无需人工干预，适合长期野外部署的无人机系统使用。
我们提出SCAR，一种基于卫星影像的长时空中视觉惯性系统自校准方法，利用地理参考的卫星影像作为持久全局参照。通过将航拍图像与公开获取的正射影像和高程模型生成的2D-3D对应关系对齐，实现相机内参与外参的联合估计。与依赖专用标定动作或人工测量控制点的方法不同，该方法借助外部地理空间数据，在实际部署条件下检测并修正标定退化。我们在两年内、六组大规模航拍任务中评估该方法，覆盖多种季节与环境条件。所有序列中，SCAR均显著优于现有基线（Kalibr、COLMAP、VINS-Mono），大幅降低中位重投影误差，并转化为更小的视觉定位旋转误差与更高的位姿精度。结果表明，SCAR可在无需人工干预的情况下，为长期空中作业提供准确、鲁棒且可复现的标定效果。
原文摘要 · Abstract (English)
We introduce SCAR, a method for long-term auto-calibration refinement of aerial visual-inertial systems that exploits georeferenced satellite imagery as a persistent global reference. SCAR estimates both intrinsic and extrinsic parameters by aligning aerial images with 2D--3D correspondences derived from publicly available orthophotos and elevation models. In contrast to existing approaches that rely on dedicated calibration maneuvers or manually surveyed ground control points, our method leverages external geospatial data to detect and correct calibration degradation under field deployment conditions. We evaluate our approach on six large-scale aerial campaigns conducted over two years under diverse seasonal and environmental conditions. Across all sequences, SCAR consistently outperforms established baselines (Kalibr, COLMAP, VINS-Mono), reducing median reprojection error by a large margin, and translating these calibration gains into substantially lower visual localization rotation errors and higher pose accuracy. These results demonstrate that SCAR provides accurate, robust, and reproducible calibration over long-term aerial operations without the need for manual intervention.
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